Assessing driving risk through unsupervised detection of anomalies in telematics time series data

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<dc:creator> Weng Chan, Ian</dc:creator>
<dc:creator>Badescu, Andrei L.</dc:creator>
<dc:creator>Sheldon Lin, X.</dc:creator>
<dc:creator>International Actuarial Association</dc:creator>
<dc:date>2025-05-12</dc:date>
<dc:description xml:lang="es">Sumario: Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model to model and analyse trip-level telematics data</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/189394.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Matemática del seguro</dc:subject>
<dc:subject xml:lang="es">Seguro de automóviles</dc:subject>
<dc:subject xml:lang="es">Telemática</dc:subject>
<dc:subject xml:lang="es">Evaluación de riesgos</dc:subject>
<dc:subject xml:lang="es">Modelo de Markov</dc:subject>
<dc:subject xml:lang="es">Análisis de datos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Assessing driving risk through unsupervised detection of anomalies in telematics time series data</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 12/05/2025 Volume 55 Issue 2 - may 2025 , p. 205 - 241</dc:relation>
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